Mean shift is an iterative procedure for locating stationary points of a density function derived from a set of samples. Although the mean shift procedure has been known for decades, it has only recently has been applied to computer vision applications, such as object tracking, image smoothing and foreground segmentation.
Mean shift clustering is an unsupervised density based nonparametric clustering technique for samples having an unknown distribution approximated by kernel density estimation. The cluster centers are located by the mean shift procedure, and the samples associated with the same local maxima of the density function cluster the samples. If label information is available, then the clustering accuracy can be improved significantly.
Weakly supervised clustering procedures can use pairwise “must-link” constraints to specify that two samples belong to the same cluster. Constraints of this form are natural in the context of graph partitioning, where edges in the graph encode pairwise relationships, such as in graph cuts and random walk segmentation.
Similarly, “cannot-link” constraints specify that two samples belong to different clusters. Hard constraints indicate that the constraints must be satisfied during clustering, whereas soft constraints are not necessarily satisfied but used as guide during clustering.
Unlike weakly supervised variants of k-means, spectral, and graph clustering methods, conventional mean shift methods do not utilize the label information to guide the clustering.